The present invention relates to systems and methods for generating, analyzing and visualizing consumer sentiments with social insights and by consumer segments. Such systems and methods enable businesses to more efficiently drive business strategies that are responsive to consumer emotions and sentiments, in order to increase one or more business objectives.
Businesses are increasingly competitive as information access becomes more widespread. In order to remain viable, businesses must rely upon advancements in marketing, price optimization, and understanding a consumer's reaction to a product or business activity. Typically, data poor mechanisms were employed in order to collect information on consumers' sentiments. These methodologies included surveys, focus groups, and other targeted research. However, there is a major drawback with these techniques due to the generally low sample size, lack of candidness on behalf of those being surveyed, and inaccurate reflection of the consumer base in the surveyed group.
As social media and other online platforms have become more prominent features in everyday lives, more and more businesses have dedicated resources in order to understand and market using these new channels into potential customers. Moreover, in addition to being a useful vehicle for delivering a message to consumers, these platforms provide unique insights into consumer sentiments.
In addition to social networks, we live in an age where vast amounts of data are being collected. These various data sources, in conjunction with newly available social network data, allows for unprecedented abilities to analyze consumer sentiment. However, analyzing this vast data pool is fraught with logistic and technical difficulties. Too often, superfluous information obscures the insights that can be gained from the data. Misdirection and false conclusions are common; thus, businesses are cautious to rely too heavily upon such “big data” analytics.
There are existing software applications for performing data analytics-based business intelligence currently. These applications permit the acquisition of data, the organization of stored data, the application of business rules to perform the analytics, and the presentation of the analytics result. In the past, such applications require the use of an expert system integrator company or highly skilled personnel in the IT department (often a luxury that only the largest companies can afford) since these tools require custom coding, custom configuration and heavy customization.
Furthermore, new technologies are now available for data storage, data acquisition, data analysis, and presentation. Big data or cloud computing (whether open-source or proprietary) are some examples of such technologies. Some of these technologies have not yet been widely adopted by the business intelligence industry. Being new, the level of expertise required to make use of these technologies is fairly high since there are fewer people familiar with these technologies. This trend drives up the cost of implementing new business intelligence systems or updating existing business intelligence systems, particularly if the business desire to make use of the new technologies.
It is therefore apparent that an urgent need exists for systems and methods generating, analyzing and visualizing consumer sentiments with social insights and by consumer segments. Such systems and methods enable businesses to more efficiently drive business strategies that are responsive to consumer emotions and sentiments, in order to increase one or more business objectives.